get.landscape.FPK.MCMC: Plot posterior distribution of macroevolutionary landscapes.

Description Usage Arguments Author(s) See Also Examples

View source: R/get.landscape.FPK.MCMC.R

Description

The function plots the median value of the macroevolutionary landscape across the posterior in a solid line and draws a polygon that streches between two quantiles of the posterior.

Usage

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get.landscape.FPK.MCMC(chain, bounds, Npts = 100, burnin = 0.1, 
  probs.CI = c(0.05, 0.95), COLOR_MEDIAN = "red", COLOR_FILL = "red", 
  transparency = 0.3, main = "Macroevolutionary landscapes MCMC", 
  ylab = "N.exp(-V)", xlab = "Trait", xlim = NULL, ylim = NULL)

Arguments

chain

An data.frame object representing the output of an MCMC chain, as obtained by MH_MCMC_FPK.

bounds

The bounds on the trait interval

Npts

The number of points used in the discretization procedure.

burnin

The percentage of generations discarded as burnin.

probs.CI

A vector of the two quantiles of the posterior distribution between which samples should be considered.

COLOR_MEDIAN

The color used to plot the median macroevolutionary landscape across the posterior.

COLOR_FILL

The color used to plot the polygon that stretches between the two quantiles of the posterior.

transparency

The transparency used for plotting the polygon

main

Title of the graph.

ylab

y label of the graph.

xlab

X label of the graph.

xlim
ylim

Author(s)

F.C. Boucher

See Also

MH_MCMC_FPK

Examples

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## Not run: 
# Simulate data: tree + continuous trait
library(geiger)
tree=sim.bdtree(stop='taxa',n=10) # tree with few tips for quick tests
tree$edge.length=100*tree$edge.length/max(branching.times(tree)) # rescale the tree 
# Simulate trait evolving on a macroevolutionary landscape with two peaks of equal heights
x=seq(from=-1.5,to=1.5,length.out=100)
bounds=c(min(x),max(x)) # the bounds we use for simulating: for technical purposes only
V6=10*(x^4-0.5*(x^2)+0.*x) # this is the evolutionary potential: it has two wells
TRAIT= Sim_FPK(tree,x0=0,V=V6,sigma=10,bounds=c(-5, 5)) 
# Run a MCMC chain to fit the FPK model
MCMC=MH_MCMC_FPK(tree,trait=TRAIT,bounds=c(5,5),Nsteps=10000,record_every=100,
  plot_every=100,Npts=20,pars_init=c(0,-4,-4,0,1),prob_update=c(0.2,0.25,0.25,0.25,0.05),
  verbose=TRUE,plot=TRUE,save_to='MCMC_FPK_test.Rdata',save_every=100,
  type_priors=c(rep('Normal',4),'Uniform'),
  shape_priors=list(c(0,10),c(0,10),c(0,10),c(0,10),NA),proposal_type='Uniform',
  proposal_sensitivity=c(0.1,0.1,0.1,0.1,1),prior.only=F)
get.landscape.FPK.MCMC(chain=MCMC,bounds=c(5,5),Npts=100,burnin=0.1,
  probs.CI=c(0.025,0.975),COLOR_MEDIAN='red',COLOR_FILL='red',transparency=0.3,
  main='Macroevolutionary landscapes MCMC',ylab='N.exp(-V)',xlab='Trait',
  xlim=NULL,ylim=NULL)  

## End(Not run)

BBMV documentation built on April 30, 2018, 9:03 a.m.